Scene sketching is to convert a scene into a simplified, abstract representation that captures the essential elements and composition of the original scene. It requires a semantic understanding of the scene and consideration of different regions within the scene. Since scenes often contain diverse visual information across various regions, such as foreground objects, background elements, and spatial divisions, dealing with these different regions poses unique difficulties. In this paper, we define a sketch as some sets of Bézier curves because of their smooth and versatile characteristics. We optimize different regions of input scene in multiple rounds. In each optimization round, the strokes sampled from the next region can seamlessly be integrated into the sketch generated in the previous optimization round. We propose an additional stroke initialization method to ensure the integrity of the scene and the convergence of optimization. A novel CLIP-based Semantic Loss and a VGG-based Feature Loss are utilized to guide our multi-round optimization. Extensive experimental results on the quality and quantity of the generated sketches confirm the effectiveness of our method.

Point clouds obtained from 3D scanning are typically incomplete, noisy, and sparse. Previous completion methods aim to generate complete point clouds, while taking into account the densification of point clouds, filling small holes, and proximity-to-surface, all through a single network. After revisiting the task, we propose SDNet, which disentangles the task based on the spatial characteristics of point clouds and formulates two sub-networks, a Dense Refiner and a Missing Generator. Given a partial input, the Dense Refiner produces a dense and clean point cloud, as a more reliable partial surface, which assists the Missing Generator to better infer the remaining point cloud structure. To promote the alignment and interaction across these two modules, we propose a Cross Fusion Unit with designed Non-Symmetrical Cross Transformers to capture geometric relationships between partial and missing regions, contributing to a complete, dense and well-aligned output. Extensive quantitative and qualitative results demonstrate that our method outperforms the state-of-the-art methods.